ML MCONF NEW: Difference between revisions

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(Documented that increasing the parameter for ML_ISTART=3 increases efficiency.)
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The use of this tag in combination with the learning algorithms is described here: [[Machine learning force field calculations: Basics#Sampling of training data and local reference configurations|here]].
The use of this tag in combination with the learning algorithms is described here: [[Machine learning force field calculations: Basics#Sampling of training data and local reference configurations|here]].
If force fields are reparameterized ({{TAG|ML_ISTART}}=3), calculations are usually more efficient, if this parameter is increased.
If force fields are reparameterized ({{TAG|ML_ISTART}}=3), calculations are usually more efficient, if this parameter is increased to values around 10-16 in particular, if the ML_AB files are large.


== Related tags and articles ==
== Related tags and articles ==

Revision as of 08:42, 26 August 2022

ML_MCONF_NEW = [integer]
Default: ML_MCONF_NEW = 5 

Description: This tag sets the number of configurations that are stored temporarily as candidates for the training data in the machine learning force field method.


Warning: This value is close to optimal for on-the-fly learning, and should usually not be changed.

The use of this tag in combination with the learning algorithms is described here: here. If force fields are reparameterized (ML_ISTART=3), calculations are usually more efficient, if this parameter is increased to values around 10-16 in particular, if the ML_AB files are large.

Related tags and articles

Examples that use this tag


ML_LMLFF, ML_MCONF, ML_CTIFOR, ML_CDOUB